ccfbupt / cag_visdial

Iterative Context-Aware Graph Inference for Visual Dialog

The overall framework of Context-Aware Graph.

This is a PyTorch implementation for Iterative Context-Aware Graph Inference for Visual Dialog, CVPR2020.

If you use this code in your research, please consider citing:

@InProceedings{Guo_2020_CVPR,
author = {Guo, Dan and Wang, Hui and Zhang, Hanwang and Zha, Zheng-Jun and Wang, Meng},
title = {Iterative Context-Aware Graph Inference for Visual Dialog},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

Requirements

This code is implemented using PyTorch v0.3.1, and provides out of the box support with CUDA 9 and CuDNN 7.

Data

1. Download the VisDial v1.0 dialog json files and images from here.
2. Download the word counts file for VisDial v1.0 train split from here.
3. Use Faster-RCNN to extract image features from here.
4. Download pre-trained GloVe word vectors from here.

Training

Train the CAG model as:

python train/train_D_1.0.py --CUDA

Evaluation

Evaluation of a trained model checkpoint can be done as follows:

python eval/evaluate.py --model_path [path_to_root]/save/XXXXX.pth --cuda

This will generate an EvalAI submission file, and you can submit the json file to online evaluation server to get the result on v1.0 test-std.

Model	NDCG	MRR	R@1	R@5	R@10	Mean
CAG	56.64	63.49	49.85	80.63	90.15	4.11

Acknowledgements

• This code began with jiasenlu/visDial.pytorch. We thank the developers for doing most of the heavy-lifting.